Langgraph Docs
- 4.3k installs
- 27.3k repo stars
- Updated August 5, 2026
- langchain-ai/deepagents
How to build stateful multi-agent systems, implement agent state machines, and integrate human review into agent workflows using LangGraph.
About
LangGraph documentation enables developers to build stateful agents and multi-agent systems using graph-based state machines. It provides essential patterns for agent orchestration, including cyclical workflows where agents loop through decision steps, human approval gates that pause execution for review, and coordination mechanisms between multiple autonomous agents. Developers use LangGraph when implementing complex agentic systems beyond simple prompt-response chains, requiring state persistence across agent steps, conditional routing between agent nodes, and human feedback integration into agent loops. Key developer workflows include defining agent state schemas, constructing directed graphs of agent operations, persisting conversation memory across interactions, and implementing execution breakpoints for human review before autonomous actions execute. Build agents as directed graphs with nodes representing discrete operations and edges controlling state transitions Define persistent state machines that maintain context across multi-step agent interactions and human interventions Implement human-in-the-loop checkpoints where agents pause execution pending human approval or fee.
- Build agents as directed graphs with nodes representing discrete operations and edges controlling state transitions
- Define persistent state machines that maintain context across multi-step agent interactions and human interventions
- Implement human-in-the-loop checkpoints where agents pause execution pending human approval or feedback
- Orchestrate multi-agent workflows with routing logic that coordinates between specialized agent nodes
- Access API reference docs for LangGraph Python client configuration, node definition, and state management methods
Langgraph Docs by the numbers
- 4,285 all-time installs (skills.sh)
- +82 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #182 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Security screen: MEDIUM risk (skills.sh audit)
- Data as of Aug 5, 2026 (Skillselion catalog sync)
langgraph-docs capabilities & compatibility
- Capabilities
- agent orchestration · state machine definition · multi agent coordination · human in the loop workflows · conditional routing · state persistence
- Works with
- anthropic · openai
- Use cases
- orchestration · planning · code review
What langgraph-docs says it does
Use when the user asks about LangGraph, graph agents, state machines, agent orchestration, LangGraph API, or needs LangGraph implementation guidance
npx skills add https://github.com/langchain-ai/deepagents --skill langgraph-docsAdd your badge
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| Installs | 4.3k |
|---|---|
| repo stars | ★ 27.3k |
| Security audit | 2 / 3 scanners passed |
| Last updated | August 5, 2026 |
| Repository | langchain-ai/deepagents ↗ |
What it does
Build stateful multi-agent workflows with human-in-the-loop patterns using LangGraph state machines and graph orchestration.
Who is it for?
Building complex multi-step agents, implementing human-in-the-loop approval workflows, coordinating multiple specialized agents, persisting agent memory across sessions.
Skip if: Simple single-turn QA, basic chatbots without state, non-agent generative tasks, training custom models.
When should I use this skill?
User asks about LangGraph, building stateful agents, agent orchestration patterns, human-in-the-loop workflows, multi-agent coordination, or implementing agent state machines.
What you get
Developers can construct agent systems as directed graphs with state persistence, conditional routing, human checkpoints, and multi-agent orchestration.
- Relevant LangGraph documentation excerpts
- Implementation guidance for graph agent patterns
By the numbers
- LangGraph provides graph-based state machine abstraction for building agents as directed acyclic graphs
- Supports human-in-the-loop via explicit breakpoints where agent execution halts pending human input
Files
langgraph-docs
Workflow
1. Fetch the Documentation Index
Use fetch_url to read: https://docs.langchain.com/llms.txt
This returns a structured list of all available documentation with descriptions.
2. Select Relevant Documentation
Identify 2-4 most relevant URLs from the index. Prioritize:
- Implementation questions — specific how-to guides
- Conceptual questions — core concept pages
- End-to-end examples — tutorials
- API details — reference docs
3. Fetch and Apply
Use fetch_url on the selected URLs, then complete the user's request using the documentation content.
If fetch_url fails or returns empty content, retry once. If it fails again, inform the user and suggest checking https://langchain-ai.github.io/langgraph/ directly.
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Pick langgraph-docs over generic web-search skills when the question is specifically LangGraph Python API implementation requiring the official docs index.
FAQ
What is LangGraph and when should I use it instead of basic LLM chains?
LangGraph enables building agents as directed graphs with persistent state, conditional routing, and human checkpoints. Use it for multi-step workflows, agent loops, human-in-the-loop systems, and multi-agent coordination.
How do I implement human approval gates in an agent workflow?
LangGraph supports breakpoints where agent execution pauses, allowing human review of agent decisions before proceeding. This integrates human feedback into autonomous agent loops.
Can I use LangGraph for multi-agent orchestration?
Yes. LangGraph enables coordinating multiple specialized agents by routing state between agent nodes, implementing conditional logic to select which agent acts next, and managing shared state across agents.
Is Langgraph Docs safe to install?
skills.sh reports 2 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.